自然资源学报 ›› 2020, Vol. 35 ›› Issue (12): 2848-2861.doi: 10.31497/zrzyxb.20201203

• 其他研究论文 • 上一篇    下一篇

基于网络数据的乡村旅游热点识别及成因分析——以江苏省为例

荣慧芳1,2(), 陶卓民1()   

  1. 1.南京师范大学地理科学学院,南京 210023
    2.池州学院资源环境学院,池州 247000
  • 收稿日期:2019-05-24 修回日期:2019-08-01 出版日期:2020-12-28 发布日期:2021-02-28
  • 作者简介:荣慧芳(1984- ),女,河南永城人,博士研究生,研究方向为旅游地理与旅游规划。E-mail: ronghuifang96@126.com
  • 基金资助:
    国家自然科学基金项目(41571139);国家自然科学基金项目(51778002);江苏省研究生科研与实践创新计划项目(KYCX19_0762)

Hotspot identification and cause analysis of rural tourism based on website data:Take Jiangsu province as an example

RONG Hui-fang1,2(), TAO Zhuo-min1()   

  1. 1. School of Geography Science, Nanjing Normal University, Nanjing 210023, China
    2. Resource Environment College, Chizhou University, Chizhou 247000, Anhui, China
  • Received:2019-05-24 Revised:2019-08-01 Online:2020-12-28 Published:2021-02-28

摘要:

基于旅游网站数据,提出一种乡村旅游热点识别方法,以江苏省为例,运用趋势面、核密度、热点分析等方法探寻乡村旅游冷热格局及演变特征,并借助地理探测器揭示其时空演化的影响因素。结果表明:(1)江苏省乡村旅游热度的时间演化规律明显,年际变化呈“S”型演变轨迹;季节变化呈“三峰四谷”特征。(2)江苏省乡村旅游热度的空间结构在2009—2017年间经历了“单核—双核—三核”的演化过程,但其空间异质性依然显著,整体表现为“南高北低,东高西低”的差异特征。(3)影响因素在不同时段的影响强度各异,交通条件、服务能力一直是主导因素,经济水平对乡村旅游发展早期的热度提升有显著影响,资源禀赋的显著性趋于下降,生态环境和政府导向的影响力逐渐增强。基于网络数据的乡村旅游热点识别为乡村旅游定量研究提供了新的研究视角,对乡村旅游资源开发与区域合作具有重要的指导意义。

关键词: 大数据, 热点识别, 演化, 成因, 江苏省

Abstract:

Rural tourism has important practical significance for optimizing the rural industrial structure, and recovering the rural economy, especially for the implementation of the rural revitalization strategy. Thus, rural tourism is not only the focus of local government and tourism enterprises, but also a hot topic in domestic and international tourism research. At the same time, with the development and popularization of the Internet, travel websites, social software and other online platforms have become important tools to obtain travel information, make travel decisions, and share travel experiences. Tourism big data provides data sources and methodological support for rural tourism research. Based on data of tourism network, this paper puts forward a method for identifying rural tourism hotspots. Taking Jiangsu province as an example, this paper uses the methods of trend surface, nuclear density estimation and hot spot analysis to explore the cold and hot patterns of rural tourism, and reveals the influencing factors of the evolution with the help of geographic detectors. The results show that: (1) The annual and seasonal changes of rural tourism heat are obvious. The annual change presents s an "S" shaped evolution track, seasonal variation is characterized by "three peaks and four valleys", but the degree of seasonal influence on different types of rural tourist attractions is slightly different. (2) The spatial structure of rural tourism in Jiangsu province experienced the evolution of "mononuclear-dual-nuclear-trinuclear" in 2009-2017, but its heterogeneity is still significant, basically maintaining the overall characteristics of "high in the south and east regions, while low in the north and west regions". The hot spots are concentrated in southern Jiangsu and gradually evolve into cold spots in the north. The evolution of spatial structure shows a trend of "expanding from the west to the north". (3) There are obvious strength differences and time variations among the influencing factors. Transportation convenience and reception capacity have always been the main influencing factors. The economic development has a significant positive impact on the early development of rural tourism, and the influence of tourism resource tends to decline. The positive influence of ecological environment and government orientation on rural tourism is increasing. Hotspot identification based on network data provides a new perspective for quantitative research of rural tourism. In terms of practicability, it is helpful to clarify the evolution characteristics of the cold and hot patterns of rural tourism so as to provide important guiding significance for rural tourism resource development and regional cooperation.

Key words: big data, hotspot identification, evolution, cause, Jiangsu province